IFPTarget: A Customized Virtual Target Identification Method Based on Protein–Ligand Interaction Fingerprinting Analyses

Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data

Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.

Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

Structure-based protein-ligand interaction fingerprints for binding affinity prediction

Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.

ProfKin: A comprehensive web server for structure-based kinase profiling

Protein kinases are central mediators of signal-transduction cascades and attractive drug targets for therapeutic intervention. Since kinases are structurally and mechanistically related to each other, kinase inhibitor selectivity is often investigated by kinase profiling and considered as an important index for drug discovery. We here describe a versatile web server termed ProfKin for structure-based kinase profiling, which is based on a kinase-ligand focused database (KinLigDB). It provides all ready-to-use 3D structure coordinates of 4219 kinase-ligand complex structures covering 297 human kinases and the associated information, particularly including binding site type, binding ligand type, interaction fingerprints, downstream molecules and related human diseases. The web server works via predicting possible binding modes for the query molecule, prioritizing the binding modes guided by an interaction fingerprint analysis method, and giving a list of ranked kinases by a comprehensive index. Users can freely select entire or part of the KinLigDB database, e.g. via subfamily and binding site type, to customize the profiling contents. The superimpositions of the predicted binding poses of the query molecule with reference binding modes can be visually inspected on the website. The additional classification attributes and phylogenetic tree are also given for each top-ranked kinase.

Deep learning in target prediction and drug repositioning: Recent advances and challenges

Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.

X-ray Structure-Guided Discovery of a Potent, Orally Bioavailable, Dual Human Indoleamine/Tryptophan 2,3-Dioxygenase (hIDO/hTDO) Inhibitor That Shows Activity in a Mouse Model of Parkinson's Disease

Human indoleamine 2,3-dioxygenase 1 (hIDO1) and tryptophan 2,3-dioxygenase (hTDO) have been closely linked to the pathogenesis of Parkinson's disease (PD); nevertheless, development of dual hIDO1 and hTDO inhibitors to evaluate their potential efficacy against PD is still lacking. Here, we report biochemical, biophysical, and computational analyses revealing that 1H-indazole-4-amines inhibit both hIDO1 and hTDO by a mechanism involving direct coordination with the heme ferrous and ferric states. Crystal structure-guided optimization led to 23, which manifested IC50 values of 0.64 and 0.04 μM to hIDO1 and hTDO, respectively, and had good pharmacokinetic properties and brain penetration in mice. 23 showed efficacy against the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced mouse motor coordination deficits, comparable to Madopar, an anti-PD medicine. Further studies revealed that different from Madopar, 23 likely has specific anti-PD mechanisms involving lowering IDO1 expression, alleviating dopaminergic neurodegeneration, reducing inflammatory cytokines and quinolinic acid in mouse brain, and increasing kynurenic acid in mouse blood.

Computational representations of protein-ligand interfaces for structure-based virtual screening

Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no "one size fits all" approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS

We describe here our tool named PyPLIF HIPPOS, which was newly developed to analyze the docking results of AutoDock Vina and PLANTS. Its predecessor, PyPLIF (https://github.com/radifar/pyplif), is a molecular interaction fingerprinting tool for the docking results of PLANTS, exclusively. Unlike its predecessor, PyPLIF HIPPOS speeds up the computational times by separating the reference generation and docking analysis. PyPLIF HIPPOS also offers more options compared to PyPLIF. PyPLIF HIPPOS for Linux is stored as the Supporting Information in this application note and can be accessed in GitHub (https://github.com/radifar/PyPLIF-HIPPOS). Additionally, we present here the application of the tool in a retrospective structure-based virtual screening campaign targeting neuraminidase.

KinomeRun: An interactive utility for kinome target screening and interaction fingerprint analysis towards holistic visualization on kinome tree

Kinases are key targets for many of the pathological conditions. Inverse screening of ligands serves as an essential mode to identify potential kinase targets in modern drug discovery research. Hence, we intend to develop KinomeRun, a robust pipeline for inverse screening and kinome tree visualization through the seamless integration of kinome structures, docking and kinome-drug interaction fingerprint analysis. In this pipeline, the hurdle of residue numbering in kinome is also resolved by creating a common index file with the conserved kinase pocket residues for comparative interaction analysis. KinomeRun can be used to screen the ligands of interest docked against multiple kinase structures in parallel around the kinase binding site and also to filter out the targets with unique interaction patterns. This automation is essential for prioritization of kinase targets that show specificity for a given drug and will also serve as a crucial tool kit for holistic approaches in kinase drug discovery. KinomeRun is developed using python and bash programming language and is distributed freely under the GNU GPL licence-3.0 and can be downloaded at https://github.com/inpacdb/KinomeRun. The tutorial videos for installation, target screening and customized filtration are available at https://www.youtube.com/playlist?list=PLuIaEFtMVgQ7v__WigQH9ilGVxrfI1LKs and also be downloaded for offline viewing from the github link.

In Silico Drug-Target Profiling

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

Identification of Glycine Receptor α3 as a Colchicine-Binding Protein

Colchicine (Col) is considered a kind of highly effective alkaloid for preventing and treating acute gout attacks (flares). However, little is known about the underlying mechanism of Col in pain treatment. We have previously developed a customized virtual target identification method, termed IFPTarget, for small-molecule target identification. In this study, by using IFPTarget and ligand similarity ensemble approach (SEA), we show that the glycine receptor alpha 3 (GlyRα3), which play a key role in the processing of inflammatory pain, is a potential target of Col. Moreover, Col binds directly to the GlyRα3 as determined by the immunoprecipitation and bio-layer interferometry assays using the synthesized Col-biotin conjugate (linked Col and biotin with polyethylene glycol). These results suggest that GlyRα3 may mediate Col-induced suppression of inflammatory pain. However, whether GlyRα3 is the functional target of Col and serves as potential therapeutic target in gouty arthritis requires further investigations.

Virtual target screening reveals rosmarinic acid and salvianolic acid A inhibiting metallo- and serine-β-lactamases

Rosmarinic acid (RA), a polyphenolic phytochemical, has broad-spectrum biological and pharmacological activity. A virtual target screening method termed IFPTarget combined with enzyme inhibition assays led to the identification of the clinically relevant metallo-β-lactamase (MBL) VIM-2 as one of unexploited targets of RA. The enzyme kinetic studies indicated that RA is a fully reversible, substrate-competitive VIM-2 inhibitor. The isothermal titration calorimetry (ITC) analyses revealed that the initial binding of RA to VIM-2 is mainly due to enthalpy contribution. Further inhibition assays with RA related compounds revealed that salvianolic acid A, a derivative of RA, manifests potent inhibition to VIM-2, more interestingly, which shows inhibitory activity against the NDM-1, another clinically relevant MBL subtype, and the serine-β-lactamase TEM-1 that is structurally and mechanistically distinct from the VIM-2 and NDM-1.

#2714, a novel active inhibitor with potent G2/M phase arrest and antitumor efficacy in preclinical models

Arresting cell cycle has been one of the most common approaches worldwide in cancer therapy. Specifically, arresting cells in the G2/M phase is a promising therapeutic approach in the battle against lung cancer. In the present study, we demonstrated the anticancer activities and possible mechanism of compound #2714, which can prompt G2/M phase arrest followed by cell apoptosis induction in Lewis lung carcinoma LL/2 cells. In vitro, #2714 significantly inhibited LL/2 cell viability in a concentration- and time-dependent manner while exhibiting few toxicities on non-cancer cells. The mechanism study showed that cell proliferation inhibition due to the treatment with #2714 correlated with G2/M phase arrest and was followed by LL/2 cell apoptosis. The characterized changes were associated with the downregulation of phosphorylated cell division cycle 25C (Cdc25C) and upregulation of p53. Apoptosis-associated activation of cleaved caspase-3 was also detected. Moreover, #2714 strongly attenuated LL/2 cell proliferation by disrupting the phosphorylation of p44/42 mitogen-activated protein kinase (MAPK). In vivo, intraperitoneal administration of #2714 (25–100 mg/kg/day) to mice bearing established tumors in xenograft models significantly prevented LL/2 tumor growth (58.1%) without detectable toxicity. Compound #2714 significantly increased apoptosis in LL/2 lung cancer cells in mice models, as observed via terminal deoxynucleotidyl transferase (TdT) dUTP nick-end labeling (TUNEL) assay, and the data from an immunohistochemical analysis showed that #2714 remarkably inhibited the proliferation and angiogenesis of lung cancer in vivo. Taken together, our data suggest that #2714 has a high potential anti-lung cancer efficacy with a pathway-specific mechanism of G2/M phase arrest and subsequent apoptosis induction both in vitro and in vivo; its potential to be an anticancer candidate warrants further investigation.

Rh(iii)-Catalyzed directed C–H carbenoid coupling reveals aromatic bisphosphonates inhibiting metallo- and Serine-β-lactamases

The C–H coupling of arenes with diazo-methylene-diphosphonates enables efficient synthesis of new diphosphonates, which exhibit dual MBL and SBL inhibition.